Abstract

A reliable subtraction of seasonal signals from the Global Positioning System (GPS) position time series is beneficial for the accuracy of derived velocities. In this research, we propose a two-stage solution of the problem of a proper determination of seasonal changes. We employ environmental loading models (atmospheric, hydrological and ocean non-tidal) with a dominant annual signal of amplitudes in their superposition of up to 12 mm and study the seasonal signal (annual and semi-annual) estimates that change over time using improved singular spectrum analysis (ISSA). Then, this deterministic model is subtracted from GPS position time series. We studied data from 376 permanent International GNSS Service (IGS) stations, derived as the official contribution to International Terrestrial Reference Frame (ITRF2014) to measure the influence of applying environmental loading models on the estimated vertical velocity. Having removed the environmental loadings directly from the position time series, we noticed the evident change in the power spectrum for frequencies between 4 and 80 cpy. Therefore, we modelled the seasonal signal in environmental models using the ISSA approach and subtracted it from GPS vertical time series to leave the noise character of the time series intact. We estimated the velocity dilution of precision (DP) as a ratio between classical Weighted Least Squares and ISSA approach. For a total number of 298 out of the 376 stations analysed, the DP was lower than 1. This indicates that when the ISSA-derived curve was removed from the GPS data, the error of velocity becomes lower than it was before.

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Acknowledgements

Anna Klos, Marta Gruszczynska and Janusz Bogusz are financed by the Polish National Science Centre, Grant No. UMO-2014/15/B/ST10/03850. Machiel Simon Bos is financially supported by Portuguese funds through FCT in the scope of the Project IDL-FCT-UID/GEO/50019/2013 and Grant Number SFRH/BPD/89923/2012. Jean-Paul Boy is partly funded by CNES (Centre National d’Etudes Spatiales), through its TOSCA program. Loading time series used here are available at EOST/IPGS loading service (http://loading.u-strasbg.fr). Maps and charts were plotted in the Generic Mapping Tool (Wessel et al. 2013). IGS time series were accessed from ftp://igs-rf.ensg.eu/pub/repro2.

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